eess.ASMar 13, 2026

Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces

Authors: Kwanghee Choi, Eunjung Yeo, Cheol Jun Cho, David R. Mortensen, David Harwath

Organizations: UT Austin · UC Berkeley · Carnegie Mellon University

Abstract

Transformer-based self-supervised speech models (S3Ms) are often described as contextualized, yet what this entails remains unclear. Here, we focus on how a single frame-level S3M representation can encode phones and their surrounding context. Prior work has shown that S3Ms represent phones compositionally; for example, phonological vectors such as voicing, bilabiality, and nasality vectors are superposed in the S3M representation of [m]. We extend this view by proposing that phonological information from a sequence of neighboring phones is also compositionally encoded in a single frame, such that vectors corresponding to previous, current, and next phones are superposed within a single frame-level representation. We show that this structure has several properties, including orthogonality between relative positions, and emergence of implicit phonetic boundaries. Together, our findings advance our understanding of context-dependent S3M representations.

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